A Lighting Pytorch Framework for Recommendation System, Easy-to-use and Easy-to-extend.

Related tags

Deep Learningpytorchctrrecommendation-algorithm
Overview

Torch-RecHub

A Lighting Pytorch Framework for Recommendation Models, Easy-to-use and Easy-to-extend.

安装

pip install torch-rechub

主要特性

  • scikit-learn风格易用的API（fit、predict），即插即用

  • 训练过程与模型定义解耦，易拓展，可针对不同类型的模型设置不同的训练机制

  • 使用Pytorch原生Dataset、DataLoader，易修改，自定义数据

  • 高度模块化，支持常见Layer（MLP、FM、FFM、target-attention、self-attention、transformer等），容易调用组装成新模型

  • 支持常见排序模型（WideDeep、DeepFM、DIN、DCN、xDeepFM等）

  • 支持常见召回模型（DSSM、YoutubeDNN、MIND、SARSRec等）

  • 丰富的多任务学习支持

    • SharedBottom、ESMM、MMOE、PLE、AITM等模型
    • GradNorm、UWL等动态loss加权机制

  • 聚焦更生态化的推荐场景

    • 冷启动
    • 延迟反馈
    • 去偏

  • 支持丰富的训练机制（对比学习、蒸馏学习等）

  • 第三方高性能开源Trainer支持（Pytorch Lighting等）

  • 更多模型正在开发中

快速使用

from torch_rechub.rmodels.ranking import WideDeep, DeepFM, DIN
from torch_rechub.trainers import CTRTrainer
from torch_rechub.basic.utils import DataGenerator

dg = DataGenerator(x, y)
train_dataloader, val_dataloader, test_dataloader = dg.generate_dataloader()

model = DeepFM(deep_features=deep_features, fm_features=fm_features, mlp_params={"dims": [256, 128], "dropout": 0.2, "activation": "relu"})

ctr_trainer = CTRTrainer(model)
ctr_trainer.fit(train_dataloader, val_dataloader)
auc = ctr_trainer.evaluate(ctr_trainer.model, test_dataloader)

多任务学习

from torch_rechub.models.multi_task import SharedBottom, ESMM, MMOE, PLE, AITM
from torch_rechub.trainers import MTLTrainer

model = MMOE(features, task_types, n_expert=3, expert_params={"dims": [64,32,16]}, tower_params_list=[{"dims": [8]}, {"dims": [8]}])

ctr_trainer = MTLTrainer(model)
ctr_trainer.fit(train_dataloader, val_dataloader)
auc = ctr_trainer.evaluate(ctr_trainer.model, test_dataloader)

Note:

所有模型均在大多数论文提及的多个知名公开数据集中测试，达到或者接近论文性能。

使用案例：Examples

每个数据集将会提供

  • 一个使用脚本，包含样本生成、模型训练与测试，并提供一套测评所用参数。
  • 一个预处理脚本，将原始数据进行预处理，转化成csv。
  • 数据格式参考文件（100条）。
  • 全量数据，统一的csv文件，提供高速网盘下载链接和原始数据链接。

初步规划TODO清单

Owner
Mincai Lai
Mincai Lai
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